OpenRaiser/Intern-Atlas huggingface dataset:
Privacy-Aware Human Mobility Prediction via Adversarial Networks
Venue: arXiv cs.LG 2022
Year: 2022

As various mobile devices and location-based services are increasingly developed in different smart city scenarios and applications, many unexpected privacy leakages have arisen due to geolocated data collection and sharing. While these geolocated data could provide a rich understanding of human mobility patterns and address various societal research questions, privacy concerns for users' sensitive information have limited their utilization. In this paper, we design and implement a novel LSTM-based adversarial mechanism with representation learning to attain a privacy-preserving feature representation of the original geolocated data (mobility data) for a sharing purpose. We quantify the utility-privacy trade-off of mobility datasets in terms of trajectory reconstruction risk, user re-identification risk, and mobility predictability. Our proposed architecture reports a Pareto Frontier analysis that enables the user to assess this trade-off as a function of Lagrangian loss weight parameters. The extensive comparison results on four representative mobility datasets demonstrate the superiority of our proposed architecture and the efficiency of the proposed privacy-preserving features extractor. Our results show that by exploring Pareto optimal setting, we can simultaneously increase both privacy (45%) and utility (32%).
ThorFI: A Novel Approach for Network Fault Injection as a Service
Venue: arXiv cs.SE 2022
Year: 2022

In this work, we present a novel fault injection solution (ThorFI) for virtual networks in cloud computing infrastructures. ThorFI is designed to provide non-intrusive fault injection capabilities for a cloud tenant, and to isolate injections from interfering with other tenants on the infrastructure. We present the solution in the context of the OpenStack cloud management platform, and release this implementation as open-source software. Finally, we present two relevant case studies of ThorFI, respectively in an NFV IMS and of a high-availability cloud application. The case studies show that ThorFI can enhance functional tests with fault injection, as in 4%-34% of the test cases the IMS is unable to handle faults; and that despite redundancy in virtual networks, faults in one virtual network segment can propagate to other segments, and can affect the throughput and response time of the cloud application as a whole, by about 3 times in the worst case.
Welfare vs. Representation in Participatory Budgeting
Venue: arXiv cs.GT 2022
Year: 2022

Participatory budgeting (PB) is a democratic process for allocating funds to projects based on the votes of members of the community. Different rules have been used to aggregate participants' votes. Past research has studied the trade-off between notions of social welfare and fairness in the multi-winner setting (a special case of participatory budgeting with identical project costs) by Lackner and Skowron (2020). But there is little understanding of this trade-off in the more general PB setting. This paper provides a theoretical and empirical study of the worst-case guarantees of several common rules to better understand the trade-off between social welfare, representation. We show that many of the guarantees from the multi-winner setting do not generalize to the PB setting, and that the introduction of costs leads to substantially worse guarantees, thereby exacerbating the welfare-representation trade-off. We extend our theoretical analysis to studying how the requirement of proportionality over voting rules affects this trade-off. We further study how the requirement of proportionality over voting rules effects the guarantees on social welfare and representation. We study the latter point also empirically, both on real and synthetic datasets. We show that variants of the recently suggested voting rule Rule-X (which satisfies proportionality) do very well in practice both with respect to social welfare and representation.
Quasi optimal anticodes: structure and invariants
Venue: arXiv cs.IT 2022
Year: 2022

It is well-known that the dimension of optimal anticodes in the rank-metric is divisible by the maximum m between the number of rows and columns of the matrices. Moreover, for a fixed k divisible by m, optimal rank-metric anticodes are the codes with least maximum rank, among those of dimension k. In this paper, we study the family of rank-metric codes whose dimension is not divisible by m and whose maximum rank is the least possible for codes of that dimension, according to the Anticode bound. As these are not optimal anticodes, we call them quasi optimal anticodes (qOACs). In addition, we call dually qOAC a qOAC whose dual is also a qOAC. We describe explicitly the structure of dually qOACs and compute their weight distributions, generalized weights, and associated q-polymatroids.
repro_eval: A Python Interface to Reproducibility Measures of System-oriented IR Experiments
Venue: arXiv cs.IR 2022
Year: 2022

In this work we introduce repro_eval - a tool for reactive reproducibility studies of system-oriented information retrieval (IR) experiments. The corresponding Python package provides IR researchers with measures for different levels of reproduction when evaluating their systems' outputs. By offering an easily extensible interface, we hope to stimulate common practices when conducting a reproducibility study of system-oriented IR experiments.
ReGNL: Rapid Prediction of GDP during Disruptive Events using Nightlights
Venue: arXiv cs.LG 2022
Year: 2022

Policy makers often make decisions based on parameters such as GDP, unemployment rate, industrial output, etc. The primary methods to obtain or even estimate such information are resource intensive and time consuming. In order to make timely and well-informed decisions, it is imperative to be able to come up with proxies for these parameters which can be sampled quickly and efficiently, especially during disruptive events, like the COVID-19 pandemic. Recently, there has been a lot of focus on using remote sensing data for this purpose. The data has become cheaper to collect compared to surveys, and can be available in real time. In this work, we present Regional GDP NightLight (ReGNL), a neural network based model which is trained on a custom dataset of historical nightlights and GDP data along with the geographical coordinates of a place, and estimates the GDP of the place, given the other parameters. Taking the case of 50 US states, we find that ReGNL is disruption-agnostic and is able to predict the GDP for both normal years (2019) and for years with a disruptive event (2020). ReGNL outperforms timeseries ARIMA methods for prediction, even during the pandemic. Following from our findings, we make a case for building infrastructures to collect and make available granular data, especially in resource-poor geographies, so that these can be leveraged for policy making during disruptive events.
Coded Compressed Sensing with List Recoverable Codes for the Unsourced Random Access
Venue: arXiv cs.IT 2022
Year: 2022

We consider a coded compressed sensing approach for the unsourced random access and replace the outer tree code proposed by Amalladinne et al. with the list recoverable code capable of correcting t errors. A finite-length random coding bound for such codes is derived. The numerical experiments in the single antenna quasi-static Rayleigh fading MAC show that transition to list recoverable codes correcting t errors improves the performance of coded compressed sensing scheme by 7-10 dB compared to the tree code-based scheme. We propose two practical constructions of outer codes. The first is a modification of the tree code. It utilizes the same code structure, and a key difference is a decoder capable of correcting up to t errors. The second is based on the Reed-Solomon codes and Guruswami-Sudan list decoding algorithm. The first scheme provides an energy efficiency very close to the random coding bound when the decoding complexity is unbounded. But for the practical parameters, the second scheme is better and improves the performance of a tree code-based scheme when the number of active users is less than 200.
Object Detection in Autonomous Vehicles: Status and Open Challenges
Venue: arXiv cs.CV 2022
Year: 2022

Object detection is a computer vision task that has become an integral part of many consumer applications today such as surveillance and security systems, mobile text recognition, and diagnosing diseases from MRI/CT scans. Object detection is also one of the critical components to support autonomous driving. Autonomous vehicles rely on the perception of their surroundings to ensure safe and robust driving performance. This perception system uses object detection algorithms to accurately determine objects such as pedestrians, vehicles, traffic signs, and barriers in the vehicle's vicinity. Deep learning-based object detectors play a vital role in finding and localizing these objects in real-time. This article discusses the state-of-the-art in object detectors and open challenges for their integration into autonomous vehicles.
Summarising and Comparing Agent Dynamics with Contrastive Spatiotemporal Abstraction
Venue: arXiv cs.AI 2022
Year: 2022

We introduce a data-driven, model-agnostic technique for generating a human-interpretable summary of the salient points of contrast within an evolving dynamical system, such as the learning process of a control agent. It involves the aggregation of transition data along both spatial and temporal dimensions according to an information-theoretic divergence measure. A practical algorithm is outlined for continuous state spaces, and deployed to summarise the learning histories of deep reinforcement learning agents with the aid of graphical and textual communication methods. We expect our method to be complementary to existing techniques in the realm of agent interpretability.
A pipeline for automated processing of Corona KH-4 (1962-1972) stereo imagery
Venue: arXiv cs.CV 2022
Year: 2022

The Corona KH-4 reconnaissance satellite missions from 1962-1972 acquired panoramic stereo imagery with high spatial resolution of 1.8-7.5 m. The potential of 800,000+ declassified Corona images has not been leveraged due to the complexities arising from handling of panoramic imaging geometry, film distortions and limited availability of the metadata required for georeferencing of the Corona imagery. This paper presents Corona Stereo Pipeline (CoSP): A pipeline for processing of Corona KH-4 stereo panoramic imagery. CoSP utlizes a deep learning based feature matcher SuperGlue to automatically match features point between Corona KH-4 images and recent satellite imagery to generate Ground Control Points (GCPs). To model the imaging geometry and the scanning motion of the panoramic KH-4 cameras, a rigorous camera model consisting of modified collinearity equations with time dependent exterior orientation parameters is employed. The results show that using the entire frame of the Corona image, bundle adjustment using well-distributed GCPs results in an average standard deviation (SD) of less than 2 pixels. The distortion pattern of image residuals of GCPs and y-parallax in epipolar resampled images suggest that film distortions due to long term storage as likely cause of systematic deviations. Compared to the SRTM DEM, the Corona DEM computed using CoSP achieved a Normalized Median Absolute Deviation (NMAD) of elevation differences of ~4 m over an area of approx. 4000 $km^2$. We show that the proposed pipeline can be applied to sequence of complex scenes involving high relief and glacierized terrain and that the resulting DEMs can be used to compute long term glacier elevation changes over large areas.
Adaptive Bézier Degree Reduction and Splitting for Computationally Efficient Motion Planning
Venue: arXiv cs.RO 2022
Year: 2022

As a parametric polynomial curve family, Bézier curves are widely used in safe and smooth motion design of intelligent robotic systems from flying drones to autonomous vehicles to robotic manipulators. In such motion planning settings, the critical features of high-order Bézier curves such as curve length, distance-to-collision, maximum curvature/velocity/acceleration are either numerically computed at a high computational cost or inexactly approximated by discrete samples. To address these issues, in this paper we present a novel computationally efficient approach for adaptive approximation of high-order Bézier curves by multiple low-order Bézier segments at any desired level of accuracy that is specified in terms of a Bézier metric. Accordingly, we introduce a new Bézier degree reduction method, called parameterwise matching reduction, that approximates Bézier curves more accurately compared to the standard least squares and Taylor reduction methods. We also propose a new Bézier metric, called the maximum control-point distance, that can be computed analytically, has a strong equivalence relation with other existing Bézier metrics, and defines a geometric relative bound between Bézier curves. We provide extensive numerical evidence to demonstrate the effectiveness of our proposed Bézier approximation approach. As a rule of thumb, based on the degree-one matching reduction error, we conclude that an $n^\text{th}$-order Bézier curve can be accurately approximated by $3(n-1)$ quadratic and $6(n-1)$ linear Bézier segments, which is fundamental for Bézier discretization.
ROS georegistration: Aerial Multi-spectral Image Simulator for the Robot Operating System
Venue: arXiv cs.RO 2022
Year: 2022

This article describes a software package called ROS georegistration intended for use with the Robot Operating System (ROS) and the Gazebo 3D simulation environment. ROSgeoregistration provides tools for the simulation, test and deployment of aerial georegistration algorithms and is made available with a link provided in the paper. A model creation package is provided which downloads multi-spectral images from the Google Earth Engine database and, if necessary, incorporates these images into a single, possibly very large, reference image. Additionally a Gazebo plugin which uses the real-time sensor pose and image formation model to generate simulated imagery using the specified reference image is provided along with related plugins for UAV relevant data. The novelty of this work is threefold: (1) this is the first system to link the massive multi-spectral imaging database of Google's Earth Engine to the Gazebo simulator, (2) this is the first example of a system that can simulate geospatially and radiometrically accurate imagery from multiple sensor views of the same terrain region, and (3) integration with other UAS tools creates a new holistic UAS simulation environment to support UAS system and subsystem development where real-world testing would generally be prohibitive. Sensed imagery and ground truth registration information is published to client applications which can receive imagery synchronously with telemetry from other payload sensors, e.g., IMU, GPS/GNSS, barometer, and windspeed sensor data. To highlight functionality, we demonstrate ROSgeoregistration for simulating Electro-Optical (EO) and Synthetic Aperture Radar (SAR) image sensors and an example use case for developing and evaluating image-based UAS position feedback, i.e., pose for image-based Guidance Navigation and Control (GNC) applications.
Enhanced Performance of Pre-Trained Networks by Matched Augmentation Distributions
Venue: arXiv cs.CV 2022
Year: 2022

There exists a distribution discrepancy between training and testing, in the way images are fed to modern CNNs. Recent work tried to bridge this gap either by fine-tuning or re-training the network at different resolutions. However re-training a network is rarely cheap and not always viable. To this end, we propose a simple solution to address the train-test distributional shift and enhance the performance of pre-trained models -- which commonly ship as a package with deep learning platforms \eg, PyTorch. Specifically, we demonstrate that running inference on the center crop of an image is not always the best as important discriminatory information may be cropped-off. Instead we propose to combine results for multiple random crops for a test image. This not only matches the train time augmentation but also provides the full coverage of the input image. We explore combining representation of random crops through averaging at different levels \ie, deep feature level, logit level, and softmax level. We demonstrate that, for various families of modern deep networks, such averaging results in better validation accuracy compared to using a single central crop per image. The softmax averaging results in the best performance for various pre-trained networks without requiring any re-training or fine-tuning whatsoever. On modern GPUs with batch processing, the paper's approach to inference of pre-trained networks, is essentially free as all images in a batch can all be processed at once.
Babylon: Reusing Bitcoin Mining to Enhance Proof-of-Stake Security
Venue: arXiv cs.CR 2022
Year: 2022

Bitcoin is the most secure blockchain in the world, supported by the immense hash power of its Proof-of-Work miners, but consumes huge amount of energy. Proof-of-Stake chains are energy-efficient, have fast finality and accountability, but face several fundamental security issues: susceptibility to non-slashable long-range safety attacks, non-slashable transaction censorship and stalling attacks and difficulty to bootstrap new PoS chains from low token valuation. We propose Babylon, a blockchain platform which combines the best of both worlds by reusing the immense Bitcoin hash power to enhance the security of PoS chains. Babylon provides a data-available timestamping service, securing PoS chains by allowing them to timestamp data-available block checkpoints, fraud proofs and censored transactions on Babylon. Babylon miners merge mine with Bitcoin and thus the platform has zero additional energy cost. The security of a Babylon-enhanced PoS protocol is formalized by a cryptoeconomic security theorem which shows slashable safety and liveness guarantees.
Effect of Human Involvement on Work Performance and Fluency in Human-Robot Collaboration for Recycling
Venue: arXiv cs.RO 2022
Year: 2022

Human-robot collaboration has significant potential in recycling due to the wide variation in the composition of recyclable products. Six participants performed a recyclable item sorting task collaborating with a robot arm equipped with a vision system. The effect of three different levels of human involvement or assistance to the robot (Level 1- occlusion removal; Level 2- optimal spacing; Level 3- optimal grip) on performance metrics such as robot accuracy, task time and subjective fluency were assessed. Results showed that human involvement had a remarkable impact on the robot's accuracy, which increased with human involvement level. Mean accuracy values were 33.3% for Level 1, 69% for Level 2 and 100% for Level 3. The results imply that for sorting processes involving diverse materials that vary in size, shape, and composition, human assistance could improve the robot's accuracy to a significant extent while also being cost-effective.
Multi-SIM support in 5G Evolution: Challenges and Opportunities
Venue: arXiv cs.NI 2022
Year: 2022

Devices with multiple Subscriber Identification Modules (SIM)s are expected to prevail over the conventional devices with only one SIM. Despite the growing demand for such devices, only proprietary solutions are available so far. To fill this gap, the Third Generation Partnership Project (3GPP) is aiming at the development of unified cross-platform solutions for multi-SIM device coordination. This paper extends the technical discussion and investigation of the 3GPP solutions for improving mobile Terminated (MT) service delivery to multi-SIM devices. Implementation trade-offs, impact on the Quality of Service(QoS), and possible future directions in 3GPP are outlined.
DFBVS: Deep Feature-Based Visual Servo
Venue: arXiv cs.RO 2022
Year: 2022

Classical Visual Servoing (VS) rely on handcrafted visual features, which limit their generalizability. Recently, a number of approaches, some based on Deep Neural Networks, have been proposed to overcome this limitation by comparing directly the entire target and current camera images. However, by getting rid of the visual features altogether, those approaches require the target and current images to be essentially similar, which precludes the generalization to unknown, cluttered, scenes. Here we propose to perform VS based on visual features as in classical VS approaches but, contrary to the latter, we leverage recent breakthroughs in Deep Learning to automatically extract and match the visual features. By doing so, our approach enjoys the advantages from both worlds: (i) because our approach is based on visual features, it is able to steer the robot towards the object of interest even in presence of significant distraction in the background; (ii) because the features are automatically extracted and matched, our approach can easily and automatically generalize to unseen objects and scenes. In addition, we propose to use a render engine to synthesize the target image, which offers a further level of generalization. We demonstrate these advantages in a robotic grasping task, where the robot is able to steer, with high accuracy, towards the object to grasp, based simply on an image of the object rendered from the camera view corresponding to the desired robot grasping pose.
TerViT: An Efficient Ternary Vision Transformer
Venue: arXiv cs.CV 2022
Year: 2022

Vision transformers (ViTs) have demonstrated great potential in various visual tasks, but suffer from expensive computational and memory cost problems when deployed on resource-constrained devices. In this paper, we introduce a ternary vision transformer (TerViT) to ternarize the weights in ViTs, which are challenged by the large loss surface gap between real-valued and ternary parameters. To address the issue, we introduce a progressive training scheme by first training 8-bit transformers and then TerViT, and achieve a better optimization than conventional methods. Furthermore, we introduce channel-wise ternarization, by partitioning each matrix to different channels, each of which is with an unique distribution and ternarization interval. We apply our methods to popular DeiT and Swin backbones, and extensive results show that we can achieve competitive performance. For example, TerViT can quantize Swin-S to 13.1MB model size while achieving above 79% Top-1 accuracy on ImageNet dataset.
VISA: An Ambiguous Subtitles Dataset for Visual Scene-Aware Machine Translation
Venue: arXiv cs.CL 2022
Year: 2022

Existing multimodal machine translation (MMT) datasets consist of images and video captions or general subtitles, which rarely contain linguistic ambiguity, making visual information not so effective to generate appropriate translations. We introduce VISA, a new dataset that consists of 40k Japanese-English parallel sentence pairs and corresponding video clips with the following key features: (1) the parallel sentences are subtitles from movies and TV episodes; (2) the source subtitles are ambiguous, which means they have multiple possible translations with different meanings; (3) we divide the dataset into Polysemy and Omission according to the cause of ambiguity. We show that VISA is challenging for the latest MMT system, and we hope that the dataset can facilitate MMT research. The VISA dataset is available at: https://github.com/ku-nlp/VISA.